An Unstructured, Random Python Cipher That Seems Unbreakable

Today, we will be cracking codes with python. While researching on this post, I came up on an article about Caesar’s cipher. Caesar’s cipher is a means of encrypting messages using a mapping from the original alphabets to the encrypted alphabets with the original alphabets shifted by some keys either to the left or the right to produce the encrypted alphabets. The author said that Caesar’s cipher, which was one of the earliest forms of cryptography, could be broken by a brute force method. I said to myself: “That’s cool. It could be broken because Caesar’s cipher has a key with a structure. What about if the key has no definite structure?” So, I decided to write a program that is inspired by Caesar’s cipher but with a random key that has no structure. Rather than use the python chr and ord functions, I decided that a better way for my concept to work was to randomize a translation table. But to have a random translation table, I needed to first create it.

 

How do you create a translation table that has no structure when mapping from source to destination strings and is random? Well, before I begin explaining how, I should explain the functions we are going to use. The functions are python’s randint, maketrans and translate functions.

The python randint function.

The python randint function is a random number generator in python and one of the methods of the python random module. It generates a random integer each time it runs. To use it, you have to import the python random module. The syntax of the randint function is random.randint(a, b) where randint generates integers between a and b inclusive. If you want an indepth coverage of the python randint function and other functions of the python random module, you could do well to read it up on an earlier post. So in my solution today, I will be using the randint function to generate python random numbers.

The next function is the maketrans function.

What is the python maketrans function?

Python has two types of maketrans functions - the static byte.maketrans method and the static str.maketrans method. The earlier belongs to byte objects and the latter to string objects. Both are used to make translation tables for mapping characters.

• Bytes.maketrans:

The syntax is bytes.maketrans(from, to). It will map each python character in the from string of bytes to its equivalent python character in the to string of bytes while making a translation table to be used by the python translate function. From and to must be bytes objects with the same length. To create a translation table that maps ‘a’ to ‘e’, ‘b’ to ‘f’, and ‘c’ to ‘g’, in bytes, we could write the following code:

original = b'abc'
end = b'efg'
translation_table = bytes.maketrans(original, end)

When we have a translation table, the work of doing the actual translation is nearly complete.

• Str.maketrans:

The syntax is str.maketrans(x[, y[, z]]) where y and z are optional arguments. When using the python str.maketrans function you are making translation tables that maps python characters or Unicode ordinals to other python characters, Unicode ordinals or None. Note that Unicode ordinals are mappings of characters as integers. For example, ordinal 97 is character ‘a’ while ordinal 98 is character ‘b’.

When only x is used as the argument in the python str.maketrans method, you must supply a dictionary to str.maketrans method to make a translation table. Note that all translation tables are dictionaries that maps the source to the destination. Here is an example:

Like before, we are mapping ‘a’ to ‘e’, ‘b’ to ‘f’, and ‘c’ to ‘g’. In the translation table, the Unicode ordinals for the characters are used to identify the characters.

What if you specify two parameters to maketrans i.e x and y. When you do so, both x and y must be python strings of equal length. You need to specify a string that contains the keys in order for maketrans to properly understand how to create the translation table. Maketrans will create a translation table mapping characters in x, the source, to characters in y at the same index. An example is below:

If you want some characters to be mapped to None in the translation table, then you have to specify the third argument, z, when calling str.maketrans. Any character in z is mapped to None. Here is an example where d is mapped to None. Any character mapped to None is deleted during the translation of the actual message.

So, I believe you now understand how to create translation tables and you know that the translation tables uses the Unicode ordinals for the characters. Therefore, instead of specifying characters, you could just write out the Unicode ordinals if you know them.

The next step is to do the translation. You use the python translate function to do the actual translation.

How to use the python translate function.

To do the actual translation from the translate table, you use the python translate function. There are two types of python translate functions, the bytes.translate and str.translate, but I suggest you stick to just str.translate because most of the messages you will be translating will be python strings.

The syntax for str.translate is str.translate(map) where str is the message you want to translate and map is the translation table you will be using to do the mapping of the characters in the message. Notice from above that the translation table is a dictionary of Unicode ordinals to Unicode ordinals, strings, or None.

What the translate function does is to take each character in the message, look for its corresponding key in the translation table. If it exists, it replaces that character in the message with its value in the translation table. If the character does not exist in the translation table, the character in the message is left as it is. If in the translation table the character is mapped to None, it is deleted in the message.

Now, that’s a mouthful. Let’s illustrate all the above with examples.

First, let’s translate a message containing the characters in the source above. For example, supposing our message is ‘abccbaaaab’, how would it be translated?

When you run the above you would notice that ‘abccbaaaab’ is translated to be ‘efggfeeeef’ since we are replacing all ‘a’ with ‘e’, all ‘b’ with ‘f’ and all ‘c’ with ‘g’.

Let’s take another example where some characters in the message do not appear in the translation table and also some characters in the translation table are mapped to None.

If you look closely at the code, you will notice that the message has four characters but the translation has just three characters. ‘a’ and ‘b’ were translated as per the translation table to ‘e’ and ‘f’ respectively. In the translation table, ‘d’ is mapped to None so in the translation it is removed. While there is an ‘e’ in the message but there is no key ‘e’ in the translation table so the ‘e’ character is left as is, untranslated.

So, we have what it takes for us to do our unstructured, random python cipher.

This is the source code. You can run the code before understanding the logic stated below just to see how it works. Run it more than once and see that each time you get a different encryption scheme.

This is the logic behind the code. We will first create our source string for the translation table. That will be all the lower case alphabets. We will then cast this source string to a list and use it as a list of values we are going to use for the destination string or replacement string. Since all the alphabets are 26 characters, we enter a loop in line 7 which will create a replacement string 26 times. For each iteration of the loop, we will create a random index between 0 and 25. The index variable serves as an index to the values_list which will be used to create the replacement strings or destination strings. When we have a random index, we will then check if that index has a value in the values_list (line 9). If it has a value, we place that value in the destination_list, adding it as a stack. That means the first value will be the replacement for ‘a’ in the source string. After placing that value in the destination_list, we then substitute its corresponding value in the values_list with None; to tell the code that we have come to that index. Each time that the index gets a value in the values_list that is None (we have already used it), it moves one step in the values_list modulo 26, looking for an unused value in the values_list until it finds one. When it finds one, it stops looking (lines 12 -19). This step of stepping through the values_list looking for a value makes the arrangement of the replacement strings unstructured or without any pattern. That makes it difficult to use a mathematical formula to crack the code. I was thinking about this when I wrote a blog post on the unbreakable code and internet security that describes one-way functions. One-way functions are unbreakable codes; they are functions that go one way and cannot be reversed. Making my replacement strings unstructured mimics this behavior.

When the destination_list is completely populated, we then convert the list to a string (line 20). So, right now, we have our source and destination or replacement strings which makes it possible to create the translation tables for encryption and decryption. (Lines 24 – 28). With the translation tables created, we do the actual encryption and decryption using a specified message and voila, it works. (Lines 31 – 36)

You will notice that each time you run the code, you will get a different encryption scheme because the translation tables are randomized. That makes it difficult to break. What anyone using the code would have to do is to run it once, save the state of the translation tables in a database and use the translation tables for encryption and decryption. The weak point of my code is protecting the translation tables from hackers laying a hold on it, otherwise I think it would be very difficult to hack this scheme.

I challenge anyone to hack it without peeking at the translation tables.

If you want the source code for the unstructured, random python cipher, you can download it here.

Using Python Lambda, All, And Any Functions To Verify A Food Menu

It was recently reported that the Russian opposition leader, Alexei Navalny, was in coma from suspected food poisoning. That was a disturbing news that made me start thinking of how to create a function that verifies a menu for good and bad foods. I did some thinking and decided that the right functions to use for the verification process were the python all, any and lambda functions.

 

But before I start describing the verification process, let us talk a little about these functions.

The Python All Function

The Python all function will return True if all the elements in an iterable are True or if the iterable is empty. The syntax for the python all function is all(iterable). If you want to know what an iterable means, you can check out this post on iterables.

Let us take some examples to demonstrate how it works. We will use a python list for the python all examples.

As you can see when you run the example above, all the elements need to be True or it needs to be an empty list for the python all function to return True.

Note that on a dictionary, the function works on the keys of the dictionary. You can make out examples of your own and try them out.

Also, on numbers, when the number is 0, it is evaluated to be False as a Boolean expression in python and True on all other numbers.

The Python Any Function

The python any function will return True if any element of the iterable is True and will return False if none of the elements is True. For an empty list it will return False. The syntax of the any function is any(iterable).

Here is a python any function example using a python list.

You can try it out on another iterable like a string or dictionary. In dictionaries, the python any function iterates through the keys.

One more python function you need to know about for us to write this code beautifully are python lambda functions.

What are python lambda functions?

Python lambda functions are anonymous python functions which are created with the lambda keyword. They can be used to replace function objects but are restricted in syntax to a single expression. Lambda expressions are just semantic sugar for a normal function definition. They also have scopes like normal functions through which they reference variables.

The syntax of a lambda function or lambda expression is “lambda parameters: expression”. The expression is first followed by the lambda keyword, parameters represent what is passed to the function and the expression is the code that you want to implement in the function. The parameter and expression are separated by a colon.

Given the attributes of python lambda functions above, let me demonstrate how we can create one. For example, if we want to write a single expression that cubes some numbers. Instead of resorting to a normal function, since it is a single expression, we can use a lambda expression inside another function, this time, the python map function. This is an example of a python function inside another function.

cubed = list(map(lambda x: x ** 3,  [1, 2,3,4]))
print(cubed)

You can see from above that the lambda function is used inside the map function and cubes each of the numbers in the list. The lambda function contains just a single expression, x ** 3.

Now that we have all we need to carry out the menu verification program, let’s begin.

The menu verification program

So, we want to write a program that when given a list of recommended foods we want to verify each of the menu for the day whether that menu should be accepted or not. For a menu to be accepted, all the foods have to be in the recommended_foods list and none of the foods should be in the junk_foods list. So, let’s have a go at a menu and see what happens as we write the code.

Elegant, not so? Be creative. Try out your own food test for the recommended and junk foods. Use the all, any, and lambda functions to test out your skills in python.

Happy pythoning.

Generating Power Through Python Generator Functions And Iterators

In my post on python iterators, I mentioned that one limitation of using python iterators in user defined objects is that they do not allow you to have more than one pass at the iterator when you have encountered the StopIteration exception. To conveniently overcome that limitation and give you programming power, the creators of python decided to design an object that is not only an iterable and iterator, but is also a function that yields values. That object is a python generator.

 

In this post, I will describe what a generator is and the advantages conferred on your programming when you use generators.

What are python generators?

Python generators are a special class of python functions that make the task of writing iterators very simple. While regular python functions will compute a value and return it, a python generator will return an iterator that returns a stream of values. To get up to speed with iterators, you can read up this post on python iterators. In regular functions you use a return statement to return a value, but in python generators you use a yield statement to indicate each element that is to be returned in a series.

The simple definition of a generator is any function that contains a yield keyword.

Let’s illustrate this with examples.

For example, take the function of computing the factors of a number.

def factors(n):
    ''' returns all the factors of n as a list '''
    results = []
    for k in range(1, n+1):
        if n % k == 0:
            results.append(k)
    return results

In the function, factors, given a number we divide it by every number between 1 and that number. Whenever any number divides it without a remainder, that number is a factor and we store that number in the results list. At the end of the iterative division, we return the results list containing all the factors of the given number.

I want you to notice the following deficiencies of regular functions like this. 1. We had to populate the results list with all the numbers, waiting until everything was complete and then store all the values in memory. That takes up time and memory space. 2. When the function returned its results, all the variables used in the namespace of the function were garbage collected or thrown away. We can not get them again unless we call the function another time. 3. We cannot pause and resume the function if we want to.

What if we had a function that has the ability to overcome the deficiencies above and has the ability to be iterable? That is where a python generator comes in. Now, let’s use a generator to compute the factors of a number this time around. Take note of where the yield keyword is placed in the generator function.

def factors(n):
    for k in range(1, n+1):
        if n % k == 0:
            yield k

Note that in the generator function, the return statement has been replaced by a yield statement. Also, we do not need to populate the results with all the factors like we did with the regular function but since the generator function produces an iterator that iterates through the values, we yield each of the factors as needed to the iterator. What this means is that since a python generator function produces a generator iterator, we could use the generator function in a for loop.

Take the following code as an example.

Notice that the generator function produced a gen_iterator that was itself an iterable since it implements the __iter__() and also the __next__() method. The for loop was only automatically calling on those methods and yielding the results from the generator iterator which yields the results from the generator function.

A python generator function can contain more than one yield statement and it yields the values following each yield statement in turn. Taking a cue from our generator function, we can optimize it with more than one yield statement owing to the fact that the quotient of a division of a number by a factor is also a factor, and also by testing values up to the square root of the number.

Notice that while the first iteration yielded the factors in sequential order, this second implementation although it is more optimized, did not. It just goes to show that a generator function remembers where it was in the scheme of things when it yields a result and resumes operation from where it left off. This goes to show that the big difference between a yield and a return statement is that when the return statement is executed, all variables are discarded from the function, but when the yield statement is executed, the state of execution of the generator is suspended and all local variables in the namespace is preserved. It then resumes execution from where it stopped on another invocation of the generator when the caller calls the __next__() method of the generator iterator.

In the code example above, I asked the for loop to call the __iter__() and __next__() methods of the generator iterator automatically. I would like you to see a visual demonstration of how this works. I would use a command line invocation of a python generator function for this. For example, say we have a generator function that generates ints up to a given number. Let us see how it would be doing this with yield.

 

You can see from the command line screenshot above that when we called ints_gen(3) in order to yield 3 integer values, it created an iterator. We know that iterators are defined by the __next__() method. So, when we call the next function on the iterator, it yields each of the 3 integers one after the order until it gets to the end and then raises StopIteration exception which every python iterator raises on getting to the end of their iteration. This is just a simplification of how the generator function works with an intermediate generator iterator.

One thing to note too is that generator functions can also have a return statement. They do not preclude a return statement. A generator function with a return statement will raise StopIteration exception when control flow goes to the return statement, ending all processing of values.

User defined classes with generators.

According to the documentation, writing your own user defined classes that act as generators can be a messy issue. You can make a workaround by reflecting on the fact that generator functions produce iterators, and the __iter__() method also produces iterators. So, what you do is make the class you want to have a generator to be an iterable that implements the __iter__() method and let it yield its results. Here is a python generator example as a workaround.

Happy pythoning.

Using A Python Iterator To Get Data

In the last post about python iterables, we discussed what it means to be an iterable – being able to participate in the for loop and implementing the __iter__() method to create iterator objects. There is another related concept in python that takes this ability to participate in python for loops a bit further. The concept of being an iterator. This is very important because people often get confused about what it means to be a python iterable from being a python iterator.

 

In fact, you are basically enabling your object to participate in python for loops or to be used to retrieve a stream of data when you implement the __iter__() method (make an object an iterable) but that is not enough because as I showed you in the user defined class in the last post, you need to implement one more method, the __next__() method to complete the process. So why you need the __next__() method is because __iter__(), which makes your object an iterable, just returns an iterator object but implementing __next__() makes it possible for you to access the elements in the iterator object and defines that object as an iterator. So, with this we are ready to define what it means to be an iterator.

What it means to be a python iterator

To be a python iterator, an object just needs to implement the __next__() method. This method helps the object to remember its state when returning the next value in the iteration, update its state so that it can point to the next value, and signals when there are no more elements in the stream by raising the StopIteration exception. That is it. An iterator is just able to remember what it is doing while retrieving a stream of data.

Python recommends that any object that implements the __next__() method should also implement __iter__() method and when doing so return the object itself. So, this makes it that python iterators are also python iterables. Remember that fact because that is where many persons get confused. We covered this in the post on iterables.

In summary, iterators are like iterables that participate in for loops or in functions like map, zip etc which need iterables and remembers where it is when retrieving items from the object.

Now that we have a definition, let’s take examples. Several built-in datatypes support iteration like lists and dictionaries, so we will use them for examples.

See what happens when you call iter() (which invokes the __iter__() method) and then next() (which invokes the __next__()) on a dictionary object which we will use as our loop in python example.

As you can see from the code above, the dictionary looped through its keys when it was used as the argument to the next method.

You can do the same thing above with any native python iterable. They were built to act as iterators.

Python has made it that when you carry out a python for loop the process of calling iter(object) and next() is automated so that you really don’t realize what is happening under the hood.

You should note that once the StopIteration exception is raised for an object, it must continue to raise that exception on subsequent calls to the next method. This is because in memory what you have is an empty container or iterator. To make the object start all over again and return the stream, you need to call iter method afresh if it is a container object like a list or dictionary, but if not, there is nothing to do but to use a python generator. This occasion is why you often do not see python iterators being used often because python generators come in handy to help you when you need multiple passes to a non-container iterator object. We will discuss python generators in the next post because they are interesting python functions, so just watch out for it.

User defined python iterators

Iterators that you define yourself in code just need to implement __iter__() which produces an iterator object and __next__() which helps you to traverse the elements in the stream of data. That’s just that; what I have been saying all along. I touched on this in the iterables discussion. This is some code that could be used to produce a user defined iterator that is based on the list datatype.

As I said before, one deficiency of iterators is that they only support one pass. If you attempt a second pass at them, they behave like empty containers. You can try it out and see for yourself. Because of this limitation on having only one pass, when I want to access the items in an object as a stream, I just use them as an iterable using python for loop. But when I want to be able to generate values, I use a generator.

Some things you can do with iterators is to materialize the iterator object as a tuple, list etc, do sequence unpacking on them, or even use the max and min functions on them.

Happy pythoning.

Python Iterables Are Not Just About Sequences

Lots of times when I read code, I see people thinking that python iterables are just about python sequences like python lists, tuples, or strings. The most culprit are python lists. When they want to create a custom class that is iterable, they would rather make the underlying data structure a list in order to make use of the methods that are supported by sequences. I want to use this post to make you understand that python iterables are not just sequences. Iterables include a whole lot of objects than just sequences.

 

First, what is an iterable?

The definition of a python iterable.

Basically, a python iterable is any object that you can loop over. The object can be a python sequence like lists, strings, tuples, bytes, or they can be python collections like dictionaries, sets, frozensets, or they can even be file objects. These are all objects that are capable of returning their members or elements one item at a time. If you so desire, you can define your own user defined objects and can make them an iterable. I will show you how in later examples of loops in python.

Also, on a practical level, you can define a python iterable as anything that can appear in a for loop. I really don’t need to give an example here but think of anything you have put on the right side of a for loop in your code and that object is an iterable. The list goes on and on. Also, anything that you can put as an argument to the zip and map functions are python iterables. Therefore, knowing how the for loop operates, we can give a technical definition of an iterable.

Technically, an iterable is any object whose class implements the __iter__() special method or if you want to specify sequence semantics, which implements the __getitem__() special method. You really need to implement __iter__() method for your iterable when you need a generalized python iterator. But if you want to play with a sequence type in your object, then all you need to do is implement the __getitem__() special method.

As I do to in all my posts, let’s illustrate the definitions above with examples. Let’s first give examples of python iterables that are not python sequences. We’ll be using the practical definition: ability to participate in python for loops.

First, we’ll show that python dictionaries are iterables. Using for loop directly on the dictionary, python iterates over the dictionary based on the keys, but it has a powerful method, items, that can help one to iterate over the keys and values at the same time.

File objects are also iterables. You can replace the ‘eba.txt’ file in the code below with any text file of your choice. All I wanted to show was that the file handling object, fh, is a python iterable since it can participate in a for loop.

with open('eba.txt') as fh:
    for line in fh:
        print(line)

Then finally what you must be familiar with, python sequences. All sequence types are iterables. But not all iterables are sequence types as we have noted above.

Python strings are iterable sequences.

Lists and tuples are sequence types and also iterable. In fact, all sequence types are iterable. They give examples of loops in python.

All the types above that are iterable are custom data types. What about user defined types? I said above that user defined types can be made iterable. How? By making them implement the __iter__() method or if you desire sequence semantics, the __getitem__() method. Let’s use the __iter__() method because later I will show you how to implement the __getitem__() method.

The Fruits class below uses a python list as the underlying data structure. We implemented the __iter__() method which returns an iterator object, itself. All implementers of this method will return themselves as iterator objects. To enable the for loop to access each of the items in the iterator object, we need to implement another method, the __next__() method. The __next__() method defines an object as a python iterator and iterators are also iterables. What the __next__() method below does is just to go through each of the items using their index, which is also a data attribute, and returning each of the items with that index. When it gets to the end of the list, it returns the Stopiteration exception to the python for loop which then stops asking for more items.

One thing I want you to note from above code is that all the built-in iterables implement the __iter__() method that is why when you explicitly call iter(object) on them, they will give you an iterator object. You can read on python iterators here.

Now, let me discuss on one special type of iterable and those are sequences.

What are sequences?

Sequences are iterables but they support looping through the items in the sequence using indices. So, everything you do with a python iterable, you can also do with a python sequence. That is why when you read code, you wonder if everyone thinks only sequences are iterables. For an object to be a sequence, it must implement the __getitem__() and __len__() special methods. I discussed using the __len__() special method on user defined objects in another post. So, all python sequences like lists, tuples, strings implement these two methods.

Let’s give an example of a user defined object that acts as an iterable by mimicking the sequence semantics. Here, the Fruits class implements the __getitem__() and __len__() methods.

This code is not different from the earlier user defined code except that this time it is implementing the __getitem__() method rather than the __iter__() method.

If you ask me, which should I use, the __iter__() method or the __getitem__() method for user defined objects? The answer is – it depends on what you want to do with your user defined objects. It is rare to see implementations of the __iter__() method because of the limitations associated with iterators and python has made it possible to produce iterators easily using generators. But it is common to implement the __getitem__() method if you want your object to behave like a sequence, or even a collection.

And if you want further functionality, you could make collections.abc.Sequence the base class for your class. When you do this, then you could be able to carry out further functionalities that sequences have like find out the count of an item, get the index of an item, find out if an item is in the object etc.

Let me give some examples. First, I imported the Sequence abstract base class from collections.abc. Then, I made it the parent class to my class, Fruits. I also implemented the __contains__() special method to be able to use the “in” operator on instances of the class. Here is some code that shows the added functionality of my new Fruits class that is separate from what the native Fruits class above could do.

We cannot end the discussion without noting how python iterables support lazy evaluation of values.

Lazy evaluation in python.

According to Wikipedia.org article on lazy evaluation strategy, this is a technique which delays the evaluation of an expression until the value is needed and which also avoids repeated evaluations. Python supports the lazy evaluation technique in iterables. For example, with the built-in python range function, we don’t need to explicitly produce all the values that are needed for the range but instead use them as when needed due to the lazy evaluation technique. This helps us to save memory. For example, take the following call to range to evaluate a million items. I decided I didn’t need to print the values beyond the 100th, so I decided to break the loop at the 101th item.

for i in range(1000000):
    if i == 101:
        break
    print(i)

If python did not use the lazy evaluation technique while producing the items, it could have produced a million items while I only required just the first 100.

Lazy evaluation is also seen when you are using the items or values functions of a dictionary. Python would only get the key or value based on when and whether you need them or not. It doesn’t just populate memory with all the keys and values. Here is a python iteration over dictionary keys and values.

fruits_dict = {'mango': 1, 'orange': 3, 'pineapple': 7, 'melon': 4}
for key, value in fruits_dict.items():
    print(key, value)

Lazy evaluation saves time and memory space. This is one feature that is very powerful in python programming.

But if on the other hand, you do need all the values from the iterator that is created during lazy evaluation, you can just cast it to a list or tuple. For example, using the range earlier, If I really needed all the million items, instead of retrieving them one at a time, I can cast it to a list and get everything at once.

range_list = list(range(1000000))

You can read up the documentation glossary on iterables and use them to your pleasure. Happy pythoning.

Python Decision Control Structures: Python while And for Loops Part 2

 

The last post discussed on the use of the python if else, and elif statements as part of a selection control structure. Today, the second part, we will discuss on repetition control structures using python while and for loops. These control flows are used when we want to reuse parts of some code a number of times based on a condition.

The looping constructs provided by python, the while and for loops, are distinct, yet sometimes they can be interchanged except in some cases. First, we will discuss the python while loop.

The python while loop.

A while loop helps one to carry out repeated execution of a block of code based on repeated testing of a Boolean condition.

The syntax of a while loop is as follows:

while condition:
    body

Just similar to the python if statement, the condition is a Boolean expression that evaluates to True or False, and the body is a block of code. The block of code can even be nested with other control structures. The while loops starts its execution by testing the Boolean condition. If the condition evaluates to True, the body of the loop is executed. After the execution of the body, the condition is retested again. If the condition evaluates to True again, another iteration of the body is done. This iteration is repeated as long as the condition evaluates to True. The moment it evaluates to False, the loop is exited and the flow of control transfers to the statement outside the python while loop.

To make sure that the while loop doesn’t run forever, it should come to a point where the condition will evaluate to False. To do this effectively, use a counter that you initialize before the loop and that is incremented or decremented inside the loop. If on the other hand you find that you didn’t do so and your loop runs forever, just press Ctrl+C to interrupt the process.

Here is a code that loops through a list of fruits and tells us whether our favorite fruit, which we have to input, is in the list of fruits. Please, pardon me that the list of recommended fruits is short.

Notice in line 5 that the python while loop is based on two Boolean expressions:

while j < len(fruits) and fruits[j] != fav_fruit:

The first Boolean expression checks to make sure we have not gone to the end of the list and the second checks by the index, j, to see if we have the fruit in our list. After the Boolean expressions, the body of the loop is just a one-liner that increments the index to the list, j. j is being used here as an index to the list to iterate through the list whenever the Boolean expressions evaluates to True; that means we have not found a match for favorite fruit in the list. There is some python if else statements after the while loop that is used as confirmation whether a match was found or not. You can see the post on python if else statements.

This code does not run forever; it terminates no matter what the user enters. Because either we will not find a favorite fruit on the list and get to the end of the list where the loop terminates or we will find a favorite fruit on the list and the loop then terminates.

Now, let’s go to the second looping structure: python’s for loop with some examples.

The python for loop.

When you want to iterate through a sequence of elements in an iterable, the for loop is more preferred to the while loop. It can be used on any structure that is iterable, whether a sequence or collection. Sequences are python strings, tuples, range, and bytes while collections are python dictionaries, sets, and frozensets. The syntax of a python for loop is:

for element in iterable:
    body

You often use the element variable in the body code; the reason why we are iterating through the iterable in the first place is to access the elements. Readers who are familiar with java programming language would realize that the python for loop is in some sense similar to the java “for each” loop.

To illustrate the way a for loop works, let us take a list of numbers, iterate through each of the numbers in the list and add them together to get a total sum.

In the code above, the variable num iterates through each of the numbers in the list of numbers in the for loop and then at the body code, it adds the numbers to total variable to give the total sum.

Let’s take another python for loop example of getting the biggest number in the list of numbers.

We first assigned biggest variable to an arbitrary number, this time 0, and then in the for loop num iterates through each of the numbers. Each time num gets the value of one of the numbers in the list. In the body of the for loop we compare num to the biggest each time and if num is bigger than the biggest, we assign that num to the biggest variable. This comparison happens each iteration through the list in the for loop.

We could achieve the above code using a while loop but we would need to use an indexing method. Indexing with for loops will be described below. But note that some collections like sets cannot be done using while loops because they cannot be indexed.

Now let’s implement a for loop using an index into the elements of the iterable.

Python for loops using an index

There are occasions where we might want to know where an element resides within an iterable. The traditional application of the for loop does not give us that benefit of location. But we can get that effect by indexing into the iterable using a python range function. The range function will generate a sequence of numbers that serve as the indices into the iterable or sequence. The syntax for the for loop is:

for element in range(len(iterable)):
    body

Note how the python length function provides the number that range will use to generate the indices for looping through the iterable. I have a post on the python length function. You can check it out to further understand the syntax above. Now, let’s take some illustrative example. Suppose we want to get the biggest number like we did above but using the indices in the loop, we could implement the code this way:

We eventually implemented the same code like before but instead of iterating directly through the numbers in the list, we used the indexing method to iterate through the numbers.

Note that the index variable above is an integer which is derived from the range of values generated by the range function.

I think we have basically covered the essential points for python while and for loops. But we will not close the chapter without talking about two important statements that have an influence on the iteration of a while and for loop – the python break and continue statements.

The python break and continue statements

Both of these statements interrupt the operation of a while or for loop but in different ways.

The break statement terminates the execution of the loop and transfers control to the next statement in the code. It is usually used to check for the trigger of a condition in the loop and when that condition is satisfied, the loop is terminated.

For example, let us say we want to loop through the list of numbers above to stop when we get a 9. This is how the code could be written. Watch how the python break statement was inserted into a control flow block.

If you run the code above, you will notice that the loop is iterating through each of the numbers until it gets triggered when num is 9. When this condition is reached, the loop terminates and the rest of the numbers are not referenced.

Use the break statement sparingly. It has great power.

The companion loop interruption statement is the python continue statement. The continue statement is usually employed when we don’t want a set of statements in the body of the loop to be executed when a condition is triggered. When triggered, control passes to the next item in the loop; the loop is not terminated.

An example will suffice.

I used a python while loop this time around. The Boolean expression in the while loop checks for when we have looped through all the elements in the numbers list. For each num we are looping, we first check if the number at that index is 0, if it is not zero, we use it to divide the numerator and print out the result, but if it is zero, we tell the program to ignore that number, don’t use it to divide the numerator, and move on to the next number in the loop using the continue statement. This is a very convenient way to program. That is why I love python.

Notice on line 6 and 10 that each time I increase the index by 1 so that the loop can proceed. This makes sure we do not find ourselves in an infinite loop that doesn’t end.

Take your knowledge to new heights. Experiment with the looping constructs introduced here. It’s a joyful thing programming in python.